Designing Stock Market Trading Systems : With And Without Soft Computing.

Intro -- Contents -- Preface -- Acknowledgements -- Introduction -- Chapter 1: Designing Stock Market Trading Systems -- 1.1 Introduction -- 1.2 Motivation -- 1.3 Scope and data -- 1.4 The efficient market hypothesis -- 1.5 The illusion of knowledge -- 1.6 Investing versus trading -- 1.6.1 Investing -- 1.6.2 Trading -- 1.7 Building a mechanical stock market trading system -- 1.8 The place of soft computing -- 1.9 How to use this book -- Chapter 2: Introduction to Trading -- 2.1 Introduction -- 2.2 Different approaches to trading -- 2.2.1 Direction of trading -- 2.2.2 Time frame of trading -- 2.2.3 Type of behaviour exploited -- 2.2.3.1 Trend-based trading -- 2.2.3.2 Breakout trading -- 2.2.3.3 Momentum trading -- 2.2.3.4 Mean reversion trading -- 2.2.3.5 High frequency trading -- 2.3 Conclusion -- 2.4 The next step -- Chapter 3: Fundamental Variables -- 3.1 Introduction -- 3.1.1 Benjamin Graham and value investing -- 3.2 Informational advantage and market efficiency -- 3.3 A note on adjustments -- 3.4 Core strategies -- 3.4.1 Intrinsic value estimates -- 3.4.2 Fundamental filters -- 3.4.3 Ranking filters -- 3.5 The elements of a fundamentals-based filter -- 3.5.1 Wealth of a firm and its shareholders -- 3.5.1.1 Book value -- 3.5.1.2 Current assets vs. current liabilities -- 3.5.1.3 Leverage metrics -- 3.5.2 Earnings capacity -- 3.5.3 Ability to generate cash -- 3.6 Fundamental ratios and industry comparisons -- 3.7 A final note on cross-country investing and research -- 3.8 The next step -- 3.9 Case Study: Analysing a variable -- 3.9.1 Introduction -- 3.9.2 Example - P/E Ratio -- 3.9.3 Wealth-Lab -- 3.9.4 SPSS -- 3.9.5 Outliers -- Chapter 4: Technical Variables -- 4.1 Introduction -- 4.1.1 Charting -- 4.1.2 Technical indicators -- 4.1.3 Other approaches -- 4.2 Charting and pattern analysis -- 4.3 Technical indicators -- 4.3.1 Intermarket analysis.

4.3.2 Moving averages -- 4.3.3 Volume -- 4.3.4 Momentum indicators -- 4.3.4.1 Moving Average Convergence/Divergence (MACD) -- 4.3.4.2 Relative Strength Indicator (RSI) -- 4.4 Alternative approaches -- 4.5 On use and misuse of technical analysis -- Case Study: Does Technical Analysis Have Any Credibility? -- Chapter 5: Soft Computing -- 5.1 Introduction -- 5.1.1 Types of soft computing -- 5.1.2 Expert systems -- 5.1.3 Case-based reasoning -- 5.1.4 Genetic algorithms -- 5.1.5 Swarm intelligence -- 5.1.6 Artificial neural networks -- 5.2 Review of research -- 5.2.1 Soft computing classifications -- 5.2.2 Research into time series prediction -- 5.2.3 Research into pattern recognition and classification -- 5.2.4 Research into optimisation -- 5.2.5 Research into ensemble approaches -- 5.3 Conclusion -- 5.4 The next step -- Chapter 6: Creating Artificial Neural Networks -- 6.1 Introduction -- 6.2 Expressing your problem -- 6.3 Partitioning data -- 6.4 Finding variables of influence -- 6.5 ANN architecture choices -- 6.6 ANN training -- 6.6.1 Momentum -- 6.6.2 Training rate -- 6.7 ANN in-sample testing -- 6.8 Conclusion -- 6.9 The next step -- Chapter 7: Trading Systems and Distributions -- 7.1 Introduction -- 7.2 Studying a group of trades -- 7.2.1 Average profitability metrics -- 7.2.1.1 The students t-test -- 7.2.1.2 The runs test -- 7.2.2 Winning metrics -- 7.2.3 Losing metrics -- 7.2.4 Summary metrics -- 7.2.5 Distributions -- 7.2.5.1 Short-term distribution -- 7.2.5.2 Medium-term distribution -- 7.2.5.3 Long-term distribution -- 7.2.6 Comparing two sets of raw trades -- 7.3 Conclusions -- 7.4 The next step -- Chapter 8: Position Sizing -- 8.1 Introduction -- 8.1.1 Fixed position sizing -- 8.1.2 Kelly method -- 8.1.3 Optimal-f -- 8.1.4 Percentage of equity -- 8.1.5 Maximum risk percentage -- 8.1.6 Martingale -- 8.1.7 Anti-martingale -- 8.2 Pyramiding.

8.3 Conclusions -- 8.4 The next step -- Chapter 9: Risk -- 9.1 Introduction -- 9.2 Trade Risk -- 9.2.1 Stop-loss orders -- 9.2.2 Using maximum adverse excursion (MAE) to select the stoploss threshold -- 9.3 Risk of ruin -- 9.4 Portfolio Risk -- 9.5 Additional portfolio metrics -- 9.6 Monte Carlo Analysis -- Case study: are stops useful in trend trading systems? -- Chapter 10: Case Studies -- 10.1 Introduction -- 10.2 A note about data -- 10.3 A note about the case studies -- 10.4 Building a technical trading system with neural networks -- 10.4.1 Splitting data -- 10.4.2 Benchmark initial rules -- 10.4.3 Identify specific problems -- 10.4.4 Identify inputs and outputs for the ANN -- 10.4.5 Train the networks -- 10.4.6 Derive money management and risk settings -- 10.4.7 In-sample benchmarking -- 10.4.8 Out-of-sample benchmarking -- 10.4.9 Decide on final product -- 10.5 Building a fundamental trading system with neural networks -- 10.5.1 Splitting data -- 10.5.2 Benchmark initial rules -- 10.5.3 Identify specific problems -- 10.5.4 Identify inputs and outputs for ANN -- 10.5.5 Train the networks -- 10.5.6 Derive money management and risk settings -- 10.5.7 In-sample benchmarking -- 10.5.8 Out-of-sample benchmarking -- 10.5.9 Decide on final product -- Final thoughts -- Appendices -- Script segments -- Bibliography -- Index.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.